01 / Discovery
Identify high-value use cases, data constraints, compliance needs, and success metrics.
Process
From Pilot to Production
Our delivery process is built for AI reliability, governance, and adoption, not just demos.
02 / System Design
Define model strategy, MCP interfaces, orchestration patterns, and failure handling.
03 / Build and Integrate
Implement agents, retrieval pipelines, and frontend experiences with progressive enhancement.
04 / Evals and Hardening
Run quality evaluations, edge-case tests, and guardrail checks before production release.
05 / Launch and Observe
Monitor quality, latency, and cost with instrumentation and actionable reporting.
06 / Optimize
Continuously improve prompts, tool logic, retrieval quality, and model selection over time.